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DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:50
摘要
A defence opinion is an essential step in criminal proceedings, yet it has not been systematically formulated or evaluated as a specific LegalAI task. Grounded in legal principles and practice, we formulate this task as generating a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. We formalize this setting as a dual-perspective generation problem and introduce DefGen-Bench, a benchmark comprising several Chinese criminal cases with expert-reviewed reference defence opinions. We evaluate eight large language models (LLMs) on this task and observe that existing models tend to mirror the defendant’s opinion, thereby overlooking more appropriate defence strategies. To address this challenge, we propose Knowledge-Enhanced Highlighted Indictment (KHI), a legal knowledge–guided input enhancement method applicable to both open- and closed-source LLMs. Experiments demonstrate consistent improvements across all evaluated LLMs, validating the effectiveness of the proposed approach.
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记录 #1 · 2026-07 · 最新观测版本
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI
A defence opinion is an essential step in criminal proceedings, yet it has not been systematically formulated or evaluated as a specific LegalAI task. Grounded in legal principles and practice, we formulate this task as generating a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. We formalize this setting as a dual-perspective generation problem and introduce DefGen-Bench, a benchmark comprising several Chinese criminal cases with expert-reviewed reference defence opinions. We evaluate eight large language models (LLMs) on this task and observe that existing models tend to mirror the defendant’s opinion, thereby overlooking more appropriate defence strategies. To address this challenge, we propose Knowledge-Enhanced Highlighted Indictment (KHI), a legal knowledge–guided input enhancement method applicable to both open- and closed-source LLMs. Experiments demonstrate consistent improvements across all evaluated LLMs, validating the effectiveness of the proposed approach.
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